In many real-world applications, data are best constructed as graphs to analyze and display. The graph is such a natural structure whose nodes and edges can be used to characterize the entities and their inner-relationships among data. Recently, several works have defined neural networks on graphs[1]}, [2]}. Kipf et al.[3]} proposed graph convolutional networks, namely GCNs, based on spectral graph theory. Veličković et al.[4]} presented graph attention networks (GATs), which aggregate features following a self-attention strategy. Though such graph neural networks have been proven successful in some of node classification tasks, they still have obvious shortcomings. One of these networks' major problems is that the edge features are not incorporated into the models. In fact, most of the current state-of-the-art graph neural networks have not consider the edge features.
